@article{miehle-etal-2022-say,
title = "When to Say What and How: Adapting the Elaborateness and Indirectness of Spoken Dialogue Systems",
author = "Miehle, Juliana and
Minker, Wolfgang and
Ultes, Stefan",
editor = "Stent, Amanda and
Eugenio, Barbara Di and
Poesio, Massimo and
Georgila, Kallirroi and
Stede, Manfred",
journal = "Dialogue {\&} Discourse",
volume = "13",
month = apr,
year = "2022",
address = "Chicago, Illinois, USA",
publisher = "University of Illinois Chicago",
url = "https://aclanthology.org/2022.dnd-13.7/",
doi = "10.5210/dad.2022.101",
pages = "1--40",
abstract = "With the aim of designing a spoken dialogue system which has the ability to adapt to the user{'}s communication idiosyncrasies, we investigate whether it is possible to carry over insights from the usage of communication styles in human-human interaction to human-computer interaction. In an extensive literature review, it is demonstrated that communication styles play an important role in human communication. Using a multi-lingual data set, we show that there is a significant correlation between the communication style of the system and the preceding communication style of the user. This is why two components that extend the standard architecture of spoken dialogue systems are presented: 1) a communication style classifier that automatically identifies the user communication style and 2) a communication style selection module that selects an appropriate system communication style. We consider the communication styles elaborateness and indirectness as it has been shown that they influence the user{'}s satisfaction and the user{'}s perception of a dialogue. We present a neural classification approach based on supervised learning for each task. Neural networks are trained and evaluated with features that can be automatically derived during an ongoing interaction in every spoken dialogue system. It is shown that both components yield solid results and outperform the baseline in form of a majority-class classifier."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="miehle-etal-2022-say">
<titleInfo>
<title>When to Say What and How: Adapting the Elaborateness and Indirectness of Spoken Dialogue Systems</title>
</titleInfo>
<name type="personal">
<namePart type="given">Juliana</namePart>
<namePart type="family">Miehle</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wolfgang</namePart>
<namePart type="family">Minker</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stefan</namePart>
<namePart type="family">Ultes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<genre authority="bibutilsgt">journal article</genre>
<relatedItem type="host">
<titleInfo>
<title>Dialogue & Discourse</title>
</titleInfo>
<originInfo>
<issuance>continuing</issuance>
<publisher>University of Illinois Chicago</publisher>
<place>
<placeTerm type="text">Chicago, Illinois, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">periodical</genre>
<genre authority="bibutilsgt">academic journal</genre>
</relatedItem>
<abstract>With the aim of designing a spoken dialogue system which has the ability to adapt to the user’s communication idiosyncrasies, we investigate whether it is possible to carry over insights from the usage of communication styles in human-human interaction to human-computer interaction. In an extensive literature review, it is demonstrated that communication styles play an important role in human communication. Using a multi-lingual data set, we show that there is a significant correlation between the communication style of the system and the preceding communication style of the user. This is why two components that extend the standard architecture of spoken dialogue systems are presented: 1) a communication style classifier that automatically identifies the user communication style and 2) a communication style selection module that selects an appropriate system communication style. We consider the communication styles elaborateness and indirectness as it has been shown that they influence the user’s satisfaction and the user’s perception of a dialogue. We present a neural classification approach based on supervised learning for each task. Neural networks are trained and evaluated with features that can be automatically derived during an ongoing interaction in every spoken dialogue system. It is shown that both components yield solid results and outperform the baseline in form of a majority-class classifier.</abstract>
<identifier type="citekey">miehle-etal-2022-say</identifier>
<identifier type="doi">10.5210/dad.2022.101</identifier>
<location>
<url>https://aclanthology.org/2022.dnd-13.7/</url>
</location>
<part>
<date>2022-04</date>
<detail type="volume"><number>13</number></detail>
<extent unit="page">
<start>1</start>
<end>40</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Journal Article
%T When to Say What and How: Adapting the Elaborateness and Indirectness of Spoken Dialogue Systems
%A Miehle, Juliana
%A Minker, Wolfgang
%A Ultes, Stefan
%J Dialogue & Discourse
%D 2022
%8 April
%V 13
%I University of Illinois Chicago
%C Chicago, Illinois, USA
%F miehle-etal-2022-say
%X With the aim of designing a spoken dialogue system which has the ability to adapt to the user’s communication idiosyncrasies, we investigate whether it is possible to carry over insights from the usage of communication styles in human-human interaction to human-computer interaction. In an extensive literature review, it is demonstrated that communication styles play an important role in human communication. Using a multi-lingual data set, we show that there is a significant correlation between the communication style of the system and the preceding communication style of the user. This is why two components that extend the standard architecture of spoken dialogue systems are presented: 1) a communication style classifier that automatically identifies the user communication style and 2) a communication style selection module that selects an appropriate system communication style. We consider the communication styles elaborateness and indirectness as it has been shown that they influence the user’s satisfaction and the user’s perception of a dialogue. We present a neural classification approach based on supervised learning for each task. Neural networks are trained and evaluated with features that can be automatically derived during an ongoing interaction in every spoken dialogue system. It is shown that both components yield solid results and outperform the baseline in form of a majority-class classifier.
%R 10.5210/dad.2022.101
%U https://aclanthology.org/2022.dnd-13.7/
%U https://doi.org/10.5210/dad.2022.101
%P 1-40
Markdown (Informal)
[When to Say What and How: Adapting the Elaborateness and Indirectness of Spoken Dialogue Systems](https://aclanthology.org/2022.dnd-13.7/) (Miehle et al., DND 2022)
ACL